Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

Summary

This paper addresses the problem of parameter equifinality in distributed hydrological models, wherein multiple parameter combinations produce equally plausible fits to observed streamflow data. The authors propose a hierarchical constraint framework that progressively reduces the space of acceptable parameter sets by incorporating regional datasets, local observations (hydrographs, snow water equivalent, groundwater depth), and expert process knowledge. Applied to Stringer Creek in Montana using the DHSVM model, the approach demonstrates that systematically integrating multiple observation types and physical constraints can meaningfully improve confidence in spatiotemporal hydrological predictions at the catchment scale.

UK applicability

The hierarchical constraint methodology is transferable to UK catchments with variable hydrogeology and climate, particularly in headwater systems where process understanding is well-established. UK catchment modelling—for water resources, flood forecasting, and land management—could benefit from this framework, though application would require UK-specific regional signatures and local observation networks.

Key measures

Number of behavioural parameter sets; goodness-of-fit metrics; spatiotemporal simulation distributions; regional hydrological signatures; snow water equivalent time series; groundwater table depth patterns

Outcomes reported

The study demonstrated that a hierarchical constraint approach—combining regional signatures, hydrograph observations, snow water equivalent measurements, and groundwater table depth patterns—effectively reduced the number of behavioural parameter sets in distributed catchment modelling. The application to Stringer Creek, Montana using the DHSVM model showed that multiple parameter sets could produce similar streamflow fits, but additional process-based constraints narrowed acceptable solutions.

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